Integrated Optimisation Model of Daily Freight Train Scheduling and Dynamic Railcar Routing Based on a Two-Layer Space-Time Network
Downloads
This paper focuses on daily freight train scheduling and dynamic railcar routing problems for rail freight transportation at the operational level. Two mixed integer linear programming models that adopted different strategies were formulated based on a continuous two-layer time-space network. We simultaneously considered the benefits of railroad company and service quality when setting the objective function. By solving the models, we can distribute the dynamic railcar flows to the train paths in the basic train timetable to obtain the daily train operation plan over a short time horizon (e.g. a day), which will be helpful for dispatchers to make decisions such as the empty railcar distribution and car routing (trip planning). Finally, we compared two models on a part of the Chinese railroad network. The results show that the second model can effectively improve the efficiency of railroad freight transportation.
Downloads
Chen C, et al. One-block train formation in large-scale railway networks: An exact model and a tree-based decomposition algorithm. Transportation Research Part B: Methodological. 2018;118:1-30. DOI: 10.1016/j.trb.2018.10.003.
Xiao J, et al. Solving the train formation plan network problem of the single-block train and two-block train using a hybrid algorithm of genetic algorithm and tabu search. Transportation Research Part C: Emerging Technologies. 2018;86:124-146. DOI: 10.1016/j.trc.2017.10.006.
Lin B, et al. Optimizing the freight train connection service network of a large-scale rail system. Transportation Research Part B: Methodological. 2012;46(5):649-667. DOI: 10.1016/j.trb.2011.12.003.
Lan Z, et al. Optimizing train formation problem with car flow routing and train routing by benders-and-price approach. IEEE Access. 2019;7(3):178496–178510. DOI: 10.1109/ACCESS.2019.2958601.
Maurice K, et al. An economic view on rerouting railway wagons in a single wagonload network to avoid congestion. European Transport Research Review. 2022;14(1). DOI: 10.1186/s12544-022-00573-y.
Brännlund U, et al. Railway timetabling using lagrangian relaxation. Transportation Science. 1998;32(4):358-369. DOI: 10.1287/trsc.32.4.358.
Caprara A, et al. Modeling and solving the train timetabling problem. Operations Research. 2002;50(5):851-861. DOI: 10.1287/opre.50.5.851.362.
Zhou X, et al. Single-track train timetabling with guaranteed optimality: Branch-and-bound algorithms with enhanced lower bounds. Transportation Research Part B. 2007;41(3):320-341. DOI: 10.1016/j.trb.2006.05.003.
Lee Y, et al. A heuristic for the train pathing and timetabling problem. Transportation Research Part B: Methodological. 2009;43(8–9):837-851. DOI: 10.1016/j.trb.2009.01.009.
Barrena E, et al. Exact formulations and algorithm for the train timetabling problem with dynamic demand. Computers & Operations Research. 2014;44(APR.):66-74. DOI: 10.1016/j.cor.2013.11.003.
Cacchiani V, et al. Scheduling extra freight trains on railway networks. Transportation Research Part B: Methodological. 2010;44(2):215-231. DOI: 10.1016/j.trb.2009.07.007.
Mu S, et al. Scheduling freight trains traveling on complex networks. Transportation Research Part B: Methodological. 2011;45(7):1103-1123. DOI: 10.1016/j.trb.2011.05.021.
Kuo A, et al. Freight train scheduling with elastic demand. Transportation Research Part E Logistics & Transportation Review. 2010;46(6):1057-1070. DOI: 10.1016/j.tre.2010.05.002.
Li S, et al. Optimized train path selection method for daily freight train scheduling. IEEE Access. 2020;8:40777-40790. DOI: 10.1109/access.2020.2976904.
Liu L, et al. A decomposition based hybrid heuristic algorithm for the joint passenger and freight train scheduling problem. Computers & Operations Research. 2017;87(nov.):165-182. DOI: 10.1016/j.cor.2017.06.009.
Zhu E, et al. Scheduled service network design for freight rail transportation. Operations Research. 2014;62(2):383-400. DOI: 10.1287/opre.2013.1254.
Haghani AE. Formulation and solution of a combined train routing and makeup, and empty car distribution model. Transportation Research Part B. 1989;23(6):433-452. DOI: 10.1016/0191-2615(89)90043-X.
Crainic TG, et al. Dynamic and stochastic models for the allocation of empty containers. Operations Research. 1993;41(1):102-126. DOI: 10.1287/opre.41.1.102.
Jordan WC, et al. A stochastic, dynamic network model for railroad car distribution. Transportation Science. 1983;17(2):123-145. DOI: 10.1287/trsc.17.2.123.
Holmberg K, et al. Improved empty freight car distribution. Transportation Science. 1998;32(2):163-173. DOI: 10.1287/trsc.32.2.163.
Gorman MF, et al. North American freight rail industry real-time optimized equipment distribution systems: State of the practice. Transportation Research Part C: Emerging Technologies. 2011;19(1):103-114. DOI: 10.1016/j.trc.2010.03.012.
Kwon OK, Martland, CD, Sussman, JM. Routing and scheduling temporal and heterogeneous freight car traffic on rail networks. Transportation Research Part E: Logistics and Transportation Review. 1998;34(2):101-115. DOI: 10.1016/S1366-5545(97)00022-7.
Anghinolfi D, et al. Freight transportation in railway networks with automated terminals: A mathematical model and MIP heuristic approaches. European Journal of Operational Research. 2011;214(3):588-594. DOI: 10.1016/j.ejor.2011.05.013.
Backåker L, et al. Trip plan generation using optimization: A benchmark of freight routing and scheduling policies within the carload service segment. Journal of Rail Transport Planning & Management. 2012;2(1-2):1-13. DOI: 10.1016/j.jrtpm.2012.06.001.
Qu Z, et al. A time-space network model based on a train diagram for predicting and controlling the traffic congestion in a station caused by an emergency. Symmetry. 2019;11(6):780. DOI: 10.3390/sym11060780.
Deng L, et al. The accumulation cost of relaxed fixed time accumulation mode. IET Intelligent Transport Systems. 2021;16(4):445-458. DOI: 10.1049/itr2.12144.
Shi T, et al. A mixed integer programming model for optimizing multi-level operations process in railroad yards. Transportation Research Part B: Methodological. 2015;80(OCT.):19-39. DOI: 10.1016/j.trb.2015.06.007.
Yang Y, et al. Collaborative optimization of car-flow organization for freight trains based on adjacent technical stations. Promet - Traffic&Transportation. 2021;33(1):117-128. DOI: 10.7307/PTT.V33I1.3601.
Copyright (c) 2024 Bowen Ma, Yuguang Wei, Bo Fang; Chunyi Li
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
